raid-bench 0.0.9

RAID is the largest and most challenging benchmark for machine-generated text detectors.

  
https://raid-bench.xyz

Open Leaderboards. Trustworthy Evaluation. Robust AI Detection.

RAID is the largest & most comprehensive dataset for evaluating AI-generated text detectors. It contains over 10 million documents spanning 11 LLMs, 11 genres, 4 decoding strategies, and 12 adversarial attacks. It is designed to be the go-to location for trustworthy third-party evaluation of popular detectors.

Installation pip install raid-bench
Example Usage
from raid import run_detection, run_evaluation from raid.utils import load_data # Define your detector function def my_detector(texts: list[str]) -> list[float]: pass # Download & Load the RAID dataset train_df = load_data(split="train") # Run your detector on the dataset predictions = run_detection(my_detector, train_df) # Evaluate your detector predictions evaluation_result = run_evaluation(predictions, train_df)

With RAID you can:

• 🔬 Train Detectors: Use our dataset to train large robust detector models
• 🔄 Evaluate Generalization: Ensure your detectors maintain high performance across popular generators and domains
• 🤝 Protect Against Adversaries: Maintain high performance under common adversarial attacks
• 📊 Compare to SOTA: Compare your detector to state-of-the-art models from academia and industry.

Dataset Overview

The RAID dataset includes over 10 million generations from the following categories:

Category	Values
Models	ChatGPT, GPT-4, GPT-3 (text-davinci-003), GPT-2 XL, Llama 2 70B (Chat), Cohere, Cohere (Chat), MPT-30B, MPT-30B (Chat), Mistral 7B, Mistral 7B (Chat)
Domains	ArXiv Abstracts, Recipes, Reddit Posts, Book Summaries, NYT News Articles, Poetry, IMDb Movie Reviews, Wikipedia, Czech News, German News, Python Code
Decoding Strategies	Greedy (T=0), Sampling (T=1), Greedy + Repetition Penalty (T=0, Θ=1.2), Sampling + Repetition Penalty (T=1, Θ=1.2)
Adversarial Attacks	Article Deletion, Homoglyph, Number Swap, Paraphrase, Synonym Swap, Misspelling, Whitespace Addition, Upper-Lower Swap, Zero-Width Space, Insert Paragraphs, Alternative Spelling

Comparison

RAID is the only dataset that covers diverse models, domains, sampling strategies, and attacks
See our ACL 2024 paper for a more detailed comparison

Download RAID

The partitions of the RAID dataset we provide are broken down as follows:

	Labels?	Domains	Dataset Size (w/o adversarial)	Dataset Size (w/ adversarial)
RAID-train	✅	News, Books, Abstracts, Reviews, Reddit, Recipes, Wikipedia, Poetry	802M	11.8G
RAID-test	❌	News, Books, Abstracts, Reviews, Reddit, Recipes, Wikipedia, Poetry	81.0M	1.22G
RAID-extra	✅	Code, Czech, German	275M	3.71G

To download RAID via the pypi package, run

from raid.utils import load_data

# Download the RAID dataset with adversarial attacks included
train_df = load_data(split="train")
test_df = load_data(split="test")
extra_df = load_data(split="extra")

# Download the RAID dataset without adversarial attacks
train_noadv_df = load_data(split="train", include_adversarial=False)
test_noadv_df = load_data(split="test", include_adversarial=False)
extra_noadv_df = load_data(split="extra", include_adversarial=False)

You can also manually download the data using wget

$ wget https://dataset.raid-bench.xyz/train.csv
$ wget https://dataset.raid-bench.xyz/test.csv
$ wget https://dataset.raid-bench.xyz/extra.csv
$ wget https://dataset.raid-bench.xyz/train_none.csv
$ wget https://dataset.raid-bench.xyz/test_none.csv
$ wget https://dataset.raid-bench.xyz/extra_none.csv

NEW: You can also now download RAID through the HuggingFace Datasets 🤗 Library

from datasets import load_dataset
raid = load_dataset("liamdugan/raid")

Leaderboard Submission

To submit to the leaderboard, you must first get predictions for your detector on the test set. You can do so using either the pypi package or the CLI:

Using Pypi

import json

from raid import run_detection, run_evaluation
from raid.utils import load_data

# Define your detector function
def my_detector(texts: list[str]) -> list[float]:
    pass

# Load the RAID test data
test_df = load_data(split="test")

# Run your detector on the dataset
predictions = run_detection(my_detector, test_df)

with open('predictions.json') as f:
    json.dump(predictions, f)

Using CLI

$ python detect_cli.py -m my_detector -d test.csv -o predictions.json

After you have the predictions.json file you must then write a metadata file for your submission. Your metadata file should use the template found in this repository at leaderboard/template-metadata.json.

Finally, fork this repository. Add your generation files to leaderboard/submissions/YOUR-DETECTOR-NAME/predictions.json and your metadata file to leaderboard/submissions/YOUR-DETECTOR-NAME/metadata.json and make a pull request to this repository.

Our GitHub bot will automatically run evaluations on the submitted predictions and commit the results to leaderboard/submissions/YOUR-DETECTOR-NAME/results.json. If all looks well, a maintainer will merge the PR and your model will appear on the leaderboards!

[!NOTE] You may submit multiple detectors in a single PR - each detector should have its own directory.

Installing from Source

If you want to run the detectors we have implemented or use our dataset generation code you should install from source. To do so first clone the repository. Then install in your virtual environment of choice

Conda:

conda create -n raid_env python=3.9.7
conda activate raid_env
pip install -r requirements.txt

venv:

python -m venv env
source env/bin/activate
pip install -r requirements.txt

Then, populate the set_api_keys.sh file with the API keys for your desired modules (OpenAI, Cohere, API detectors, etc.). After that, run source set_api_keys.sh to set the API key evironment variables.

To apply a detector to the dataset through our CLI run detect_cli.py and evaluate_cli.py. These wrap around the run_detection and run_evaluation functions from the pypi package. The options are listed below. See detectors/detector.py for a list of valid detector names.

$ python detect_cli.py -h
  -m, --model           The name of the detector model you wish to run
  -d, --data_path       The path to the csv file with the dataset
  -o, --output_path     The path to write the result JSON file

$ python evaluate_cli.py -h
  -r, --results_path    The path to the detection result JSON to evaluate
  -d, --data_path       The path to the csv file with the dataset
  -o, --output_path     The path to write the result JSON file
  -t, --target_fpr      The target FPR to evaluate at (Default: 0.05)

Example:

$ python detect_cli.py -m gltr -d train.csv -o gltr_predictions.json
$ python evaluate_cli.py -i gltr_predictions.json -d train.csv -o gltr_result.json

The output of evaluate_cli.py will be a JSON file containing the accuracy of the detector on each split of the RAID dataset at the target false positive rate as well as the thresholds found for the detector.

Running custom detectors via CLI

If you would like to implement your own detector and still run it via the CLI, you must add it to detectors/detector.py so that it can be called via command line argument.

Citation

If you use our code or findings in your research, please cite us as:

@inproceedings{dugan-etal-2024-raid,
    title = "{RAID}: A Shared Benchmark for Robust Evaluation of Machine-Generated Text Detectors",
    author = "Dugan, Liam  and
      Hwang, Alyssa  and
      Trhl{\'\i}k, Filip  and
      Zhu, Andrew  and
      Ludan, Josh Magnus  and
      Xu, Hainiu  and
      Ippolito, Daphne  and
      Callison-Burch, Chris",
    booktitle = "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2024",
    address = "Bangkok, Thailand",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2024.acl-long.674",
    pages = "12463--12492",
}

Acknowledgements

This research is supported in part by the Office of the Director of National Intelligence (ODNI), Intelligence Advanced Research Projects Activity (IARPA), via the HIATUS Program contract #2022-22072200005. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies, either expressed or implied, of ODNI, IARPA, or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for governmental purposes notwithstanding any copyright annotation therein.